基于混合注意力机制的肺结节假阳性降低

False Positive Reduction of Pulmonary Nodules Based on Mixed Attentional Mechanism

为了解决肺结节CAD系统候选结节检测阶段高假阳性问题,本文提出一种基于混合注意力机制的肺结节假阳性降低方法。该方法可作为目前假阳性降低阶段最常用的3D CNN分类模型的替代方案,能有效回避3D CNN模型参数量及计算量大的问题。该方法将三维候选结节切片数据看作切片序列,使用时序分割模型,结合改进的包含混合注意力模块的2D Resnet-18骨干网络,在使用2D CNN的基础上,有效学习三维切片数据的时空特征。相对于3D CNN结构的肺结节分类模型,本文提出的方法在降低模型参数量和推理时间的基础上,提高了结节分类的准确率。

In order to solve the problem of high false positives in the candidate detection stage of pulmonary nodules CAD system,this paper proposes a method to reduce false positives of pulmonary nodules based on mixed attention mechanism.The method can be used as an alternative to the most commonly used 3D CNN classification model at the stage of false positive reduction.It can effectively avoid the problems of large number of parameters and computation in 3D CNN model.In this method,the 3D candidate nodule data is viewed as a slice sequence,and the temporal segment networks model is used in combination with the improved 2D ResNet-18 backbone network which contains mixed attention modules.On the basis of using 2D CNN,the spatial and temporal characteristics of the 3D slice data are effectively studied.Compared with the 3D CNN structure model for pulmonary nodules classification,the method proposed in this paper not only improves the accuracy of nodules classification but also reduces the number of model parameters and the inference time.